PyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph Embeddings
Abstract
Recently, knowledge graph embeddings (KGEs) have received significant attention, and several software libraries have been developed for training and evaluation. While each of them addresses specific needs, we report on a community effort to a re-design and re-implementation of PyKEEN, one of the early KGE libraries. PyKEEN 1.0 enables users to compose knowledge graph embedding models based on a wide range of interaction models, training approaches, loss functions, and permits the explicit modeling of inverse relations. It allows users to measure each component's influence individually on the model's performance. Besides, an automatic memory optimization has been realized in order to optimally exploit the provided hardware. Through the integration of Optuna, extensive hyper-parameter optimization (HPO) functionalities are provided.
Cite
Text
Ali et al. "PyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph Embeddings." Journal of Machine Learning Research, 2021.Markdown
[Ali et al. "PyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph Embeddings." Journal of Machine Learning Research, 2021.](https://mlanthology.org/jmlr/2021/ali2021jmlr-pykeen/)BibTeX
@article{ali2021jmlr-pykeen,
title = {{PyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph Embeddings}},
author = {Ali, Mehdi and Berrendorf, Max and Hoyt, Charles Tapley and Vermue, Laurent and Sharifzadeh, Sahand and Tresp, Volker and Lehmann, Jens},
journal = {Journal of Machine Learning Research},
year = {2021},
pages = {1-6},
volume = {22},
url = {https://mlanthology.org/jmlr/2021/ali2021jmlr-pykeen/}
}